Importing¶
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import xarray as xr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.metrics import root_mean_squared_error as rmse
from tqdm.auto import tqdm
import random
import salishsea_tools.viz_tools as sa_vi
Datasets Preparation¶
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def datasets_preparation(dataset):
drivers = np.stack([np.ravel(dataset['Temperature_(0m-15m)']),
np.ravel(dataset['Temperature_(15m-100m)']), np.ravel(dataset['Salinity_(0m-15m)']),
np.ravel(dataset['Salinity_(15m-100m)']), np.ravel(dataset['Nitrate_(0m-15m)']),
np.ravel(dataset['Nitrate_(15m-100m)']),
np.tile(dataset.x, len(dataset.y)),
np.tile(dataset.y, len(dataset.x))])
indx = np.where(~np.isnan(drivers).any(axis=0))
drivers = drivers[:,indx[0]]
diat = np.ravel(dataset['Diatom_Production_Rate'])
diat = diat[indx[0]]
return(drivers, diat, indx)
Regressor¶
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def regressor (inputs, targets):
inputs = inputs.transpose()
# Regressor
scale = preprocessing.StandardScaler()
inputs = scale.fit_transform(inputs)
X_train, _, y_train, _ = train_test_split(inputs, targets, train_size=0.35)
drivers = None
diat = None
inputs = None
targets = None
model = make_pipeline(scale, GradientBoostingRegressor(n_estimators=200))
regr = BaggingRegressor(model, n_estimators=12, n_jobs=4).fit(X_train, y_train)
return (regr)
Regressor 2¶
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def regressor2 (inputs, targets, variable_name):
inputs = inputs.transpose()
# Regressor
scale = preprocessing.StandardScaler()
inputs2 = scale.fit_transform(inputs)
outputs_test = regr.predict(inputs2)
m = scatter_plot(targets, outputs_test, variable_name)
r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
rms = rmse(targets, outputs_test)
return (r, rms, m)
Regressor 3¶
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def regressor3 (inputs, targets):
inputs = inputs.transpose()
# Regressor
scale = preprocessing.StandardScaler()
inputs2 = scale.fit_transform(inputs)
outputs_test = regr.predict(inputs2)
# compute slope m and intercept b
m, b = np.polyfit(targets, outputs_test, deg=1)
r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
rms = rmse(targets, outputs_test)
return (r, rms, m)
Regressor 4¶
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def regressor4 (inputs, targets, variable_name):
inputs = inputs.transpose()
# Regressor
scale = preprocessing.StandardScaler()
inputs2 = scale.fit_transform(inputs)
outputs = regr.predict(inputs2)
# Post processing
indx2 = np.full((len(diat_i.y)*len(diat_i.x)),np.nan)
indx2[indx[0]] = outputs
model = np.reshape(indx2,(len(diat_i.y),len(diat_i.x)))
m = scatter_plot(targets, outputs, variable_name + str(dates[i].date()))
# Preparation of the dataarray
model = xr.DataArray(model,
coords = {'y': diat_i.y, 'x': diat_i.x},
dims = ['y','x'],
attrs=dict( long_name = variable_name + "Concentration",
units="mmol m-2"),)
plotting3(targets, model, diat_i, variable_name)
Printing¶
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def printing (targets, outputs, m):
print ('The amount of data points is', outputs.size)
print ('The slope of the best fitting line is ', np.round(m,3))
print ('The correlation coefficient is:', np.round(np.corrcoef(targets, outputs)[0][1],3))
print (' The root mean square error is:', rmse(targets,outputs))
Scatter Plot¶
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def scatter_plot(targets, outputs, variable_name):
# compute slope m and intercept b
m, b = np.polyfit(targets, outputs, deg=1)
printing(targets, outputs, m)
fig, ax = plt.subplots(2, figsize=(5,10), layout='constrained')
ax[0].scatter(targets,outputs, alpha = 0.2, s = 10)
lims = [np.min([ax[0].get_xlim(), ax[0].get_ylim()]),
np.max([ax[0].get_xlim(), ax[0].get_ylim()])]
# plot fitted y = m*x + b
ax[0].axline(xy1=(0, b), slope=m, color='r')
ax[0].set_xlabel('targets')
ax[0].set_ylabel('outputs')
ax[0].set_xlim(lims)
ax[0].set_ylim(lims)
ax[0].set_aspect('equal')
ax[0].plot(lims, lims,linestyle = '--',color = 'k')
h = ax[1].hist2d(targets,outputs, bins=100, cmap='jet',
range=[lims,lims], cmin=0.1, norm='log')
ax[1].plot(lims, lims,linestyle = '--',color = 'k')
# plot fitted y = m*x + b
ax[1].axline(xy1=(0, b), slope=m, color='r')
ax[1].set_xlabel('targets')
ax[1].set_ylabel('outputs')
ax[1].set_aspect('equal')
fig.colorbar(h[3],ax=ax[1], location='bottom')
fig.suptitle(variable_name)
plt.show()
return (m)
Plotting¶
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def plotting(variable, name):
plt.plot(years,variable, marker = '.', linestyle = '')
plt.xlabel('Years')
plt.ylabel(name)
plt.show()
Plotting 2¶
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def plotting2(variable,title):
fig, ax = plt.subplots()
scatter= ax.scatter(dates,variable, marker='.', c=pd.DatetimeIndex(dates).month)
ax.legend(handles=scatter.legend_elements()[0], labels=['February','March','April'])
fig.suptitle('Daily ' + title + ' (15 Feb - 30 Apr)')
fig.show()
Plotting 3¶
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def plotting3(targets, model, variable, variable_name):
fig, ax = plt.subplots(2,2, figsize = (10,15))
cmap = plt.get_cmap('cubehelix')
cmap.set_bad('gray')
variable.plot(ax=ax[0,0], cmap=cmap, vmin = targets.min(), vmax =targets.max(), cbar_kwargs={'label': variable_name + ' Concentration [mmol m-2]'})
model.plot(ax=ax[0,1], cmap=cmap, vmin = targets.min(), vmax = targets.max(), cbar_kwargs={'label': variable_name + ' Concentration [mmol m-2]'})
((variable-model) / variable * 100).plot(ax=ax[1,0], cmap=cmap, cbar_kwargs={'label': variable_name + ' Concentration [percentage]'})
plt.subplots_adjust(left=0.1,
bottom=0.1,
right=0.95,
top=0.95,
wspace=0.35,
hspace=0.35)
sa_vi.set_aspect(ax[0,0])
sa_vi.set_aspect(ax[0,1])
sa_vi.set_aspect(ax[1,0])
ax[0,0].title.set_text(variable_name + ' (targets)')
ax[0,1].title.set_text(variable_name + ' (outputs)')
ax[1,0].title.set_text('targets - outputs')
ax[1,1].axis('off')
fig.suptitle(str(dates[i].date()))
plt.show()
Training (Random Points)¶
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ds = xr.open_dataset('/data/ibougoudis/MOAD/files/integrated_model_var_old.nc')
ds = ds.isel(time_counter = (np.arange(0, len(ds.Diatom.time_counter),2)),
y=(np.arange(ds.y[0], ds.y[-1], 5)),
x=(np.arange(ds.x[0], ds.x[-1], 5)))
dates = pd.DatetimeIndex(ds['time_counter'].values)
drivers, diat, _ = datasets_preparation(ds)
regr = regressor(drivers, diat)
Other Years (Anually)¶
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years = range (2007,2024)
r_all = []
rms_all = []
slope_all = []
for year in tqdm(range (2007,2024)):
dataset = ds.sel(time_counter=str(year))
drivers, diat, _ = datasets_preparation(dataset)
r, rms, m = regressor2(drivers, diat, 'Diatom ' + str(year))
r_all.append(r)
rms_all.append(rms)
slope_all.append(m)
plotting(np.transpose(r_all), 'Correlation Coefficient')
plotting(np.transpose(rms_all), 'Root Mean Square Error')
plotting (np.transpose(slope_all), 'Slope of the best fitting line')
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The amount of data points is 70794 The slope of the best fitting line is 0.514 The correlation coefficient is: 0.804 The root mean square error is: 9.860691862466333e-07
The amount of data points is 70794 The slope of the best fitting line is 0.567 The correlation coefficient is: 0.722 The root mean square error is: 1.0082039152321887e-06
The amount of data points is 68931 The slope of the best fitting line is 0.495 The correlation coefficient is: 0.745 The root mean square error is: 1.0484691844255212e-06
The amount of data points is 70794 The slope of the best fitting line is 0.513 The correlation coefficient is: 0.724 The root mean square error is: 1.0535132154323285e-06
The amount of data points is 68931 The slope of the best fitting line is 0.495 The correlation coefficient is: 0.743 The root mean square error is: 1.1026195744059351e-06
The amount of data points is 70794 The slope of the best fitting line is 0.495 The correlation coefficient is: 0.748 The root mean square error is: 1.0571987752736729e-06
The amount of data points is 70794 The slope of the best fitting line is 0.535 The correlation coefficient is: 0.728 The root mean square error is: 1.1088305128109784e-06
The amount of data points is 68931 The slope of the best fitting line is 0.564 The correlation coefficient is: 0.729 The root mean square error is: 1.1016923502002949e-06
The amount of data points is 70794 The slope of the best fitting line is 0.493 The correlation coefficient is: 0.585 The root mean square error is: 1.3206460370519641e-06
The amount of data points is 70794 The slope of the best fitting line is 0.581 The correlation coefficient is: 0.756 The root mean square error is: 1.0844969210169734e-06
The amount of data points is 68931 The slope of the best fitting line is 0.573 The correlation coefficient is: 0.726 The root mean square error is: 9.1344151557344e-07
The amount of data points is 70794 The slope of the best fitting line is 0.658 The correlation coefficient is: 0.719 The root mean square error is: 9.721463985278978e-07
The amount of data points is 68931 The slope of the best fitting line is 0.648 The correlation coefficient is: 0.747 The root mean square error is: 9.931052222113597e-07
The amount of data points is 70794 The slope of the best fitting line is 0.481 The correlation coefficient is: 0.678 The root mean square error is: 1.0948012521742677e-06
The amount of data points is 70794 The slope of the best fitting line is 0.591 The correlation coefficient is: 0.811 The root mean square error is: 8.958361029626088e-07
The amount of data points is 68931 The slope of the best fitting line is 0.555 The correlation coefficient is: 0.724 The root mean square error is: 9.742071105877143e-07
The amount of data points is 70794 The slope of the best fitting line is 0.524 The correlation coefficient is: 0.718 The root mean square error is: 1.0509766413340182e-06
Other Years (Daily)¶
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r_all2 = np.array([])
rms_all2 = np.array([])
slope_all2 = np.array([])
for i in tqdm(range (0, len(ds.time_counter))):
dataset = ds.isel(time_counter=i)
drivers, diat, _ = datasets_preparation(dataset)
r, rms, m = regressor3(drivers, diat)
r_all2 = np.append(r_all2,r)
rms_all2 = np.append(rms_all2,rms)
slope_all2 = np.append(slope_all2,m)
plotting2(r_all2, 'Correlation Coefficients')
plotting2(rms_all2, 'Root Mean Square Errors')
plotting2(slope_all2, 'Slope of the best fitting line')
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Daily Maps¶
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maps = random.sample(range(0,len(ds.time_counter)),10)
for i in tqdm(maps):
dataset = ds.isel(time_counter=i)
drivers, diat, indx = datasets_preparation(dataset)
diat_i = dataset['Diatom_Production_Rate']
regressor4(drivers, diat, 'Diatom Production Rate ')
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The amount of data points is 1863 The slope of the best fitting line is 0.701 The correlation coefficient is: 0.633 The root mean square error is: 1.796589326299975e-06
The amount of data points is 1863 The slope of the best fitting line is 0.65 The correlation coefficient is: 0.273 The root mean square error is: 2.376407453188609e-06
The amount of data points is 1863 The slope of the best fitting line is 0.736 The correlation coefficient is: 0.725 The root mean square error is: 1.0378832013691575e-06
The amount of data points is 1863 The slope of the best fitting line is 0.596 The correlation coefficient is: 0.711 The root mean square error is: 2.1151240662632372e-06
The amount of data points is 1863 The slope of the best fitting line is 0.575 The correlation coefficient is: 0.585 The root mean square error is: 9.606572531131704e-07
The amount of data points is 1863 The slope of the best fitting line is 0.853 The correlation coefficient is: 0.711 The root mean square error is: 1.1732026484837925e-06
The amount of data points is 1863 The slope of the best fitting line is 0.787 The correlation coefficient is: 0.65 The root mean square error is: 1.251610783228305e-06
The amount of data points is 1863 The slope of the best fitting line is -0.13 The correlation coefficient is: -0.101 The root mean square error is: 2.39109543919693e-06
The amount of data points is 1863 The slope of the best fitting line is 0.557 The correlation coefficient is: 0.318 The root mean square error is: 1.6548887349847792e-06
The amount of data points is 1863 The slope of the best fitting line is 1.193 The correlation coefficient is: 0.588 The root mean square error is: 2.141834942613649e-06
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